codebase-memory-quality

Name: codebase-memory-quality
Availability: InStock
Author: pepsi1978

Identify dead code and refactor candidates using static graph analysis.

1|Updated Feb 9, 2026

Search Tags:#refactor #code-analysis #dead-code #call-graph #code-cleanup #graph-queries #complexity-analysis

Ready to run

npx skills add https://github.com/Pepsi1978/proggs --skill codebase-memory-quality

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: codebase-memory-quality
Source: https://github.com/Pepsi1978/proggs/tree/main/claude-code-setup/commands/codebase-memory-quality
Command: npx skills add https://github.com/Pepsi1978/proggs --skill codebase-memory-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill identifies dead code, unreferenced functions, and refactor candidates to reduce codebase bloat and improve maintainability.

Core Features & Use Cases

Dead code detection: Find functions with zero inbound CALLS edges (excluding entry points) to surface unused code.
High-complexity & coupling analysis: Locate functions with high fan-in/fan-out and files with strong change coupling to guide refactors.
Use Case: When given a large codebase, run a memory-aware quality pass to surface candidates for cleanup and targeted optimization.

Quick Start

Provide a targeted dead-code analysis across the current repository to surface dead functions and refactor opportunities.

Frequently Asked Questions about codebase-memory-quality

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I find dead code and unreferenced functions in a large codebase?▼

You find dead code by using static graph analysis to identify functions with zero inbound CALLS edges while excluding entry points. This approach surfaces unreferenced functions and cleanup opportunities to reduce codebase bloat during maintenance.

What is the best way to identify refactor candidates using call graph analysis?▼

Call graph analysis identifies refactor candidates by locating functions with high fan-in and fan-out along with files exhibiting strong change coupling. This highlights high-complexity and highly coupled areas that require targeted optimization to improve maintainability.

Can I run a complexity analysis on a large software project without missing entry points?▼

Yes, complexity analysis on large software projects explicitly excludes entry points when filtering graph-degree data. This ensures that legitimate application starting functions are not misidentified as dead code during static graph queries.

How do you verify dead code candidates surfaced by graph-degree filters?▼

Dead code candidates surfaced by graph-degree filters are verified through built-in verification steps provided for each candidate. This ensures that functions lacking inbound CALLS edges are genuinely unreferenced before removal from the codebase.

When do I need static graph analysis for code cleanup instead of manual inspection?▼

Static graph analysis is needed for code cleanup in large software projects where manual inspection is unscalable. It systematically maps Function CALLS edges to surface dead code and refactor candidates that would otherwise remain hidden during maintenance.